Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations2000000
Missing cells2806671
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 GiB
Average record size in memory845.0 B

Variable types

Numeric9
Categorical10
DateTime1
Boolean1

Alerts

Age has 31194 (1.6%) missing values Missing
Annual Income has 74809 (3.7%) missing values Missing
Marital Status has 30865 (1.5%) missing values Missing
Number of Dependents has 182802 (9.1%) missing values Missing
Occupation has 597200 (29.9%) missing values Missing
Health Score has 123525 (6.2%) missing values Missing
Previous Claims has 606831 (30.3%) missing values Missing
Credit Score has 229333 (11.5%) missing values Missing
Customer Feedback has 130100 (6.5%) missing values Missing
Premium Amount has 800000 (40.0%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
Previous Claims has 508239 (25.4%) zeros Zeros
Vehicle Age has 102232 (5.1%) zeros Zeros

Reproduction

Analysis started2025-01-02 14:58:54.137025
Analysis finished2025-01-02 15:02:02.040429
Duration3 minutes and 7.9 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct2000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999999.5
Minimum0
Maximum1999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:02.264047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99999.95
Q1499999.75
median999999.5
Q31499999.2
95-th percentile1899999
Maximum1999999
Range1999999
Interquartile range (IQR)999999.5

Descriptive statistics

Standard deviation577350.41
Coefficient of variation (CV)0.5773507
Kurtosis-1.2
Mean999999.5
Median Absolute Deviation (MAD)500000
Skewness-6.6744919 × 10-17
Sum1.999999 × 1012
Variance3.333335 × 1011
MonotonicityStrictly increasing
2025-01-02T16:02:02.566331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999983 1
 
< 0.1%
1999982 1
 
< 0.1%
1999981 1
 
< 0.1%
1999980 1
 
< 0.1%
1999979 1
 
< 0.1%
1999978 1
 
< 0.1%
1999977 1
 
< 0.1%
1999976 1
 
< 0.1%
1999975 1
 
< 0.1%
1999974 1
 
< 0.1%
Other values (1999990) 1999990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1999999 1
< 0.1%
1999998 1
< 0.1%
1999997 1
< 0.1%
1999996 1
< 0.1%
1999995 1
< 0.1%
1999994 1
< 0.1%
1999993 1
< 0.1%
1999992 1
< 0.1%
1999991 1
< 0.1%
1999990 1
< 0.1%

Age
Real number (ℝ)

Missing 

Distinct47
Distinct (%)< 0.1%
Missing31194
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean41.141914
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:02.823304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median41
Q353
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.539099
Coefficient of variation (CV)0.32908286
Kurtosis-1.1947603
Mean41.141914
Median Absolute Deviation (MAD)12
Skewness-0.011542001
Sum81000447
Variance183.3072
MonotonicityNot monotonic
2025-01-02T16:02:03.057516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
53 43923
 
2.2%
61 43804
 
2.2%
64 43453
 
2.2%
39 43397
 
2.2%
43 43263
 
2.2%
57 43214
 
2.2%
33 43162
 
2.2%
62 42919
 
2.1%
46 42911
 
2.1%
47 42852
 
2.1%
Other values (37) 1535908
76.8%
ValueCountFrequency (%)
18 40639
2.0%
19 41299
2.1%
20 41896
2.1%
21 41446
2.1%
22 41877
2.1%
23 38791
1.9%
24 41001
2.1%
25 40373
2.0%
26 41348
2.1%
27 40635
2.0%
ValueCountFrequency (%)
64 43453
2.2%
63 40538
2.0%
62 42919
2.1%
61 43804
2.2%
60 41009
2.1%
59 41794
2.1%
58 42466
2.1%
57 43214
2.2%
56 42391
2.1%
55 41920
2.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.7 MiB
Male
1003660 
Female
996340 

Length

Max length6
Median length4
Mean length4.99634
Min length4

Characters and Unicode

Total characters9992680
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 1003660
50.2%
Female 996340
49.8%

Length

2025-01-02T16:02:03.302800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:03.511210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
male 1003660
50.2%
female 996340
49.8%

Most occurring characters

ValueCountFrequency (%)
e 2996340
30.0%
a 2000000
20.0%
l 2000000
20.0%
M 1003660
 
10.0%
F 996340
 
10.0%
m 996340
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9992680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2996340
30.0%
a 2000000
20.0%
l 2000000
20.0%
M 1003660
 
10.0%
F 996340
 
10.0%
m 996340
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9992680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2996340
30.0%
a 2000000
20.0%
l 2000000
20.0%
M 1003660
 
10.0%
F 996340
 
10.0%
m 996340
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9992680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2996340
30.0%
a 2000000
20.0%
l 2000000
20.0%
M 1003660
 
10.0%
F 996340
 
10.0%
m 996340
 
10.0%

Annual Income
Real number (ℝ)

Missing 

Distinct97540
Distinct (%)5.1%
Missing74809
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean32768.681
Minimum1
Maximum149997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:03.738007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1207
Q18021
median23957
Q344641
95-th percentile104563
Maximum149997
Range149996
Interquartile range (IQR)36620

Descriptive statistics

Standard deviation32188.136
Coefficient of variation (CV)0.98228354
Kurtosis1.785627
Mean32768.681
Median Absolute Deviation (MAD)17217
Skewness1.4680117
Sum6.308597 × 1010
Variance1.0360761 × 109
MonotonicityNot monotonic
2025-01-02T16:02:04.054600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7073 1751
 
0.1%
16054 1698
 
0.1%
24897 1572
 
0.1%
14094 1513
 
0.1%
15983 1464
 
0.1%
7991 1429
 
0.1%
13982 1425
 
0.1%
16076 1394
 
0.1%
16891 1304
 
0.1%
17091 1271
 
0.1%
Other values (97530) 1910370
95.5%
(Missing) 74809
 
3.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 9
< 0.1%
3 8
< 0.1%
5 7
< 0.1%
7 3
 
< 0.1%
8 5
 
< 0.1%
10 4
 
< 0.1%
11 17
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
149997 6
 
< 0.1%
149996 32
< 0.1%
149995 8
 
< 0.1%
149994 4
 
< 0.1%
149993 6
 
< 0.1%
149992 13
< 0.1%
149991 5
 
< 0.1%
149990 8
 
< 0.1%
149989 3
 
< 0.1%
149987 5
 
< 0.1%

Marital Status
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing30865
Missing (%)1.5%
Memory size237.3 MiB
Single
659096 
Married
656488 
Divorced
653551 

Length

Max length8
Median length7
Mean length6.997184
Min length6

Characters and Unicode

Total characters13778400
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowDivorced
3rd rowDivorced
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 659096
33.0%
Married 656488
32.8%
Divorced 653551
32.7%
(Missing) 30865
 
1.5%

Length

2025-01-02T16:02:04.643648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:04.875547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
single 659096
33.5%
married 656488
33.3%
divorced 653551
33.2%

Most occurring characters

ValueCountFrequency (%)
i 1969135
14.3%
e 1969135
14.3%
r 1966527
14.3%
d 1310039
9.5%
S 659096
 
4.8%
n 659096
 
4.8%
l 659096
 
4.8%
g 659096
 
4.8%
a 656488
 
4.8%
M 656488
 
4.8%
Other values (4) 2614204
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13778400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1969135
14.3%
e 1969135
14.3%
r 1966527
14.3%
d 1310039
9.5%
S 659096
 
4.8%
n 659096
 
4.8%
l 659096
 
4.8%
g 659096
 
4.8%
a 656488
 
4.8%
M 656488
 
4.8%
Other values (4) 2614204
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13778400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1969135
14.3%
e 1969135
14.3%
r 1966527
14.3%
d 1310039
9.5%
S 659096
 
4.8%
n 659096
 
4.8%
l 659096
 
4.8%
g 659096
 
4.8%
a 656488
 
4.8%
M 656488
 
4.8%
Other values (4) 2614204
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13778400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1969135
14.3%
e 1969135
14.3%
r 1966527
14.3%
d 1310039
9.5%
S 659096
 
4.8%
n 659096
 
4.8%
l 659096
 
4.8%
g 659096
 
4.8%
a 656488
 
4.8%
M 656488
 
4.8%
Other values (4) 2614204
19.0%

Number of Dependents
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing182802
Missing (%)9.1%
Memory size229.2 MiB
3.0
369220 
4.0
366608 
0.0
362926 
2.0
359478 
1.0
358966 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5451594
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 369220
18.5%
4.0 366608
18.3%
0.0 362926
18.1%
2.0 359478
18.0%
1.0 358966
17.9%
(Missing) 182802
9.1%

Length

2025-01-02T16:02:05.091630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:05.284343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 369220
20.3%
4.0 366608
20.2%
0.0 362926
20.0%
2.0 359478
19.8%
1.0 358966
19.8%

Most occurring characters

ValueCountFrequency (%)
0 2180124
40.0%
. 1817198
33.3%
3 369220
 
6.8%
4 366608
 
6.7%
2 359478
 
6.6%
1 358966
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5451594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2180124
40.0%
. 1817198
33.3%
3 369220
 
6.8%
4 366608
 
6.7%
2 359478
 
6.6%
1 358966
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5451594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2180124
40.0%
. 1817198
33.3%
3 369220
 
6.8%
4 366608
 
6.7%
2 359478
 
6.6%
1 358966
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5451594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2180124
40.0%
. 1817198
33.3%
3 369220
 
6.8%
4 366608
 
6.7%
2 359478
 
6.6%
1 358966
 
6.6%

Education Level
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size239.4 MiB
Master's
506370 
PhD
505975 
Bachelor's
505457 
High School
482198 

Length

Max length11
Median length10
Mean length7.9638165
Min length3

Characters and Unicode

Total characters15927633
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor's
2nd rowMaster's
3rd rowHigh School
4th rowBachelor's
5th rowBachelor's

Common Values

ValueCountFrequency (%)
Master's 506370
25.3%
PhD 505975
25.3%
Bachelor's 505457
25.3%
High School 482198
24.1%

Length

2025-01-02T16:02:05.594721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:05.823791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
master's 506370
20.4%
phd 505975
20.4%
bachelor's 505457
20.4%
high 482198
19.4%
school 482198
19.4%

Most occurring characters

ValueCountFrequency (%)
h 1975828
12.4%
s 1518197
 
9.5%
o 1469853
 
9.2%
r 1011827
 
6.4%
a 1011827
 
6.4%
' 1011827
 
6.4%
e 1011827
 
6.4%
l 987655
 
6.2%
c 987655
 
6.2%
M 506370
 
3.2%
Other values (9) 4434767
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15927633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 1975828
12.4%
s 1518197
 
9.5%
o 1469853
 
9.2%
r 1011827
 
6.4%
a 1011827
 
6.4%
' 1011827
 
6.4%
e 1011827
 
6.4%
l 987655
 
6.2%
c 987655
 
6.2%
M 506370
 
3.2%
Other values (9) 4434767
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15927633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 1975828
12.4%
s 1518197
 
9.5%
o 1469853
 
9.2%
r 1011827
 
6.4%
a 1011827
 
6.4%
' 1011827
 
6.4%
e 1011827
 
6.4%
l 987655
 
6.2%
c 987655
 
6.2%
M 506370
 
3.2%
Other values (9) 4434767
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15927633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 1975828
12.4%
s 1518197
 
9.5%
o 1469853
 
9.2%
r 1011827
 
6.4%
a 1011827
 
6.4%
' 1011827
 
6.4%
e 1011827
 
6.4%
l 987655
 
6.2%
c 987655
 
6.2%
M 506370
 
3.2%
Other values (9) 4434767
27.8%

Occupation
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing597200
Missing (%)29.9%
Memory size237.4 MiB
Employed
471324 
Self-Employed
470636 
Unemployed
460840 

Length

Max length13
Median length10
Mean length10.334517
Min length8

Characters and Unicode

Total characters14497260
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-Employed
2nd rowSelf-Employed
3rd rowSelf-Employed
4th rowEmployed
5th rowEmployed

Common Values

ValueCountFrequency (%)
Employed 471324
23.6%
Self-Employed 470636
23.5%
Unemployed 460840
23.0%
(Missing) 597200
29.9%

Length

2025-01-02T16:02:06.048939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:06.264798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
employed 471324
33.6%
self-employed 470636
33.5%
unemployed 460840
32.9%

Most occurring characters

ValueCountFrequency (%)
e 2334276
16.1%
l 1873436
12.9%
p 1402800
9.7%
o 1402800
9.7%
m 1402800
9.7%
d 1402800
9.7%
y 1402800
9.7%
E 941960
6.5%
S 470636
 
3.2%
f 470636
 
3.2%
Other values (3) 1392316
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14497260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2334276
16.1%
l 1873436
12.9%
p 1402800
9.7%
o 1402800
9.7%
m 1402800
9.7%
d 1402800
9.7%
y 1402800
9.7%
E 941960
6.5%
S 470636
 
3.2%
f 470636
 
3.2%
Other values (3) 1392316
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14497260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2334276
16.1%
l 1873436
12.9%
p 1402800
9.7%
o 1402800
9.7%
m 1402800
9.7%
d 1402800
9.7%
y 1402800
9.7%
E 941960
6.5%
S 470636
 
3.2%
f 470636
 
3.2%
Other values (3) 1392316
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14497260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2334276
16.1%
l 1873436
12.9%
p 1402800
9.7%
o 1402800
9.7%
m 1402800
9.7%
d 1402800
9.7%
y 1402800
9.7%
E 941960
6.5%
S 470636
 
3.2%
f 470636
 
3.2%
Other values (3) 1392316
9.6%

Health Score
Real number (ℝ)

Missing 

Distinct811360
Distinct (%)43.2%
Missing123525
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean25.613559
Minimum1.6465608
Maximum58.975914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:06.534717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.6465608
5-th percentile7.2914169
Q115.918658
median24.579581
Q334.52391
95-th percentile47.614835
Maximum58.975914
Range57.329353
Interquartile range (IQR)18.605252

Descriptive statistics

Standard deviation12.204827
Coefficient of variation (CV)0.47649867
Kurtosis-0.7850883
Mean25.613559
Median Absolute Deviation (MAD)9.2384519
Skewness0.28239675
Sum48063203
Variance148.9578
MonotonicityNot monotonic
2025-01-02T16:02:06.868024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.92724142 207
 
< 0.1%
19.8697009 202
 
< 0.1%
22.95540237 182
 
< 0.1%
25.90765016 182
 
< 0.1%
20.63784183 158
 
< 0.1%
27.8450064 156
 
< 0.1%
23.95570971 154
 
< 0.1%
10.9581528 151
 
< 0.1%
27.9294023 151
 
< 0.1%
24.85813464 144
 
< 0.1%
Other values (811350) 1874788
93.7%
(Missing) 123525
 
6.2%
ValueCountFrequency (%)
1.646560764 1
 
< 0.1%
2.012237182 1
 
< 0.1%
2.024415229 3
< 0.1%
2.036747412 1
 
< 0.1%
2.039338266 1
 
< 0.1%
2.039744021 1
 
< 0.1%
2.050052716 1
 
< 0.1%
2.053457869 1
 
< 0.1%
2.056558808 2
< 0.1%
2.060175622 1
 
< 0.1%
ValueCountFrequency (%)
58.97591405 1
< 0.1%
58.88603451 1
< 0.1%
58.5696892 1
< 0.1%
58.4524782 1
< 0.1%
58.40100949 1
< 0.1%
57.98884782 1
< 0.1%
57.95735079 1
< 0.1%
57.92381001 1
< 0.1%
57.90318089 1
< 0.1%
57.85252539 1
< 0.1%

Location
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.6 MiB
Suburban
668732 
Rural
668067 
Urban
663201 

Length

Max length8
Median length5
Mean length6.003098
Min length5

Characters and Unicode

Total characters12006196
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowSuburban
4th rowRural
5th rowRural

Common Values

ValueCountFrequency (%)
Suburban 668732
33.4%
Rural 668067
33.4%
Urban 663201
33.2%

Length

2025-01-02T16:02:07.139113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:07.367102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
suburban 668732
33.4%
rural 668067
33.4%
urban 663201
33.2%

Most occurring characters

ValueCountFrequency (%)
u 2005531
16.7%
b 2000665
16.7%
r 2000000
16.7%
a 2000000
16.7%
n 1331933
11.1%
S 668732
 
5.6%
R 668067
 
5.6%
l 668067
 
5.6%
U 663201
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12006196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 2005531
16.7%
b 2000665
16.7%
r 2000000
16.7%
a 2000000
16.7%
n 1331933
11.1%
S 668732
 
5.6%
R 668067
 
5.6%
l 668067
 
5.6%
U 663201
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12006196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 2005531
16.7%
b 2000665
16.7%
r 2000000
16.7%
a 2000000
16.7%
n 1331933
11.1%
S 668732
 
5.6%
R 668067
 
5.6%
l 668067
 
5.6%
U 663201
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12006196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 2005531
16.7%
b 2000665
16.7%
r 2000000
16.7%
a 2000000
16.7%
n 1331933
11.1%
S 668732
 
5.6%
R 668067
 
5.6%
l 668067
 
5.6%
U 663201
 
5.5%

Policy Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size240.1 MiB
Premium
669475 
Comprehensive
665822 
Basic
664703 

Length

Max length13
Median length7
Mean length8.332763
Min length5

Characters and Unicode

Total characters16665526
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowComprehensive
3rd rowPremium
4th rowBasic
5th rowPremium

Common Values

ValueCountFrequency (%)
Premium 669475
33.5%
Comprehensive 665822
33.3%
Basic 664703
33.2%

Length

2025-01-02T16:02:07.632232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:07.832678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
premium 669475
33.5%
comprehensive 665822
33.3%
basic 664703
33.2%

Most occurring characters

ValueCountFrequency (%)
e 2666941
16.0%
m 2004772
12.0%
i 2000000
12.0%
r 1335297
 
8.0%
s 1330525
 
8.0%
P 669475
 
4.0%
u 669475
 
4.0%
C 665822
 
4.0%
o 665822
 
4.0%
p 665822
 
4.0%
Other values (6) 3991575
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16665526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2666941
16.0%
m 2004772
12.0%
i 2000000
12.0%
r 1335297
 
8.0%
s 1330525
 
8.0%
P 669475
 
4.0%
u 669475
 
4.0%
C 665822
 
4.0%
o 665822
 
4.0%
p 665822
 
4.0%
Other values (6) 3991575
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16665526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2666941
16.0%
m 2004772
12.0%
i 2000000
12.0%
r 1335297
 
8.0%
s 1330525
 
8.0%
P 669475
 
4.0%
u 669475
 
4.0%
C 665822
 
4.0%
o 665822
 
4.0%
p 665822
 
4.0%
Other values (6) 3991575
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16665526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2666941
16.0%
m 2004772
12.0%
i 2000000
12.0%
r 1335297
 
8.0%
s 1330525
 
8.0%
P 669475
 
4.0%
u 669475
 
4.0%
C 665822
 
4.0%
o 665822
 
4.0%
p 665822
 
4.0%
Other values (6) 3991575
24.0%

Previous Claims
Real number (ℝ)

Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing606831
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean1.0035624
Minimum0
Maximum9
Zeros508239
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:08.035220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.98282561
Coefficient of variation (CV)0.97933684
Kurtosis0.75296634
Mean1.0035624
Median Absolute Deviation (MAD)1
Skewness0.90556377
Sum1398132
Variance0.96594619
MonotonicityNot monotonic
2025-01-02T16:02:08.227036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 508239
25.4%
1 501692
25.1%
2 279761
14.0%
3 81764
 
4.1%
4 17689
 
0.9%
5 3411
 
0.2%
6 506
 
< 0.1%
7 86
 
< 0.1%
8 12
 
< 0.1%
9 9
 
< 0.1%
(Missing) 606831
30.3%
ValueCountFrequency (%)
0 508239
25.4%
1 501692
25.1%
2 279761
14.0%
3 81764
 
4.1%
4 17689
 
0.9%
5 3411
 
0.2%
6 506
 
< 0.1%
7 86
 
< 0.1%
8 12
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
9 9
 
< 0.1%
8 12
 
< 0.1%
7 86
 
< 0.1%
6 506
 
< 0.1%
5 3411
 
0.2%
4 17689
 
0.9%
3 81764
 
4.1%
2 279761
14.0%
1 501692
25.1%
0 508239
25.4%

Vehicle Age
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.5706896
Minimum0
Maximum19
Zeros102232
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:08.439846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7745923
Coefficient of variation (CV)0.6033622
Kurtosis-1.2062471
Mean9.5706896
Median Absolute Deviation (MAD)5
Skewness-0.020204736
Sum19141293
Variance33.345917
MonotonicityNot monotonic
2025-01-02T16:02:08.680776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 103983
 
5.2%
11 103039
 
5.2%
0 102232
 
5.1%
18 102134
 
5.1%
10 101852
 
5.1%
14 101642
 
5.1%
15 101222
 
5.1%
19 100979
 
5.0%
12 100863
 
5.0%
16 100642
 
5.0%
Other values (10) 981403
49.1%
ValueCountFrequency (%)
0 102232
5.1%
1 95397
4.8%
2 99865
5.0%
3 98619
4.9%
4 97111
4.9%
5 99266
5.0%
6 96650
4.8%
7 99200
5.0%
8 97307
4.9%
9 99921
5.0%
ValueCountFrequency (%)
19 100979
5.0%
18 102134
5.1%
17 103983
5.2%
16 100642
5.0%
15 101222
5.1%
14 101642
5.1%
13 98067
4.9%
12 100863
5.0%
11 103039
5.2%
10 101852
5.1%

Credit Score
Real number (ℝ)

Missing 

Distinct550
Distinct (%)< 0.1%
Missing229333
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean592.91651
Minimum300
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:08.956394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile341
Q1468
median595
Q3721
95-th percentile822
Maximum849
Range549
Interquartile range (IQR)253

Descriptive statistics

Standard deviation150.03571
Coefficient of variation (CV)0.25304694
Kurtosis-1.0906138
Mean592.91651
Median Absolute Deviation (MAD)127
Skewness-0.11368412
Sum1.0498577 × 109
Variance22510.714
MonotonicityNot monotonic
2025-01-02T16:02:09.230315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
431 7142
 
0.4%
434 7111
 
0.4%
713 6698
 
0.3%
757 6668
 
0.3%
437 6458
 
0.3%
613 6288
 
0.3%
584 6275
 
0.3%
658 6212
 
0.3%
607 6199
 
0.3%
734 6193
 
0.3%
Other values (540) 1705423
85.3%
(Missing) 229333
 
11.5%
ValueCountFrequency (%)
300 1459
0.1%
301 2587
0.1%
302 1993
0.1%
303 1805
0.1%
304 1433
0.1%
305 1162
 
0.1%
306 1433
0.1%
307 2145
0.1%
308 3044
0.2%
309 2217
0.1%
ValueCountFrequency (%)
849 2948
0.1%
848 3733
0.2%
847 3441
0.2%
846 3072
0.2%
845 2847
0.1%
844 3275
0.2%
843 3499
0.2%
842 3152
0.2%
841 3155
0.2%
840 1309
 
0.1%

Insurance Duration
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.018511
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:09.479112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5941017
Coefficient of variation (CV)0.51690665
Kurtosis-1.2371694
Mean5.018511
Median Absolute Deviation (MAD)2
Skewness-0.0080417665
Sum10037007
Variance6.7293638
MonotonicityNot monotonic
2025-01-02T16:02:09.705635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 229966
11.5%
1 224532
11.2%
8 222790
11.1%
7 222513
11.1%
5 221016
11.1%
3 220260
11.0%
4 220181
11.0%
6 219702
11.0%
2 219037
11.0%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
1 224532
11.2%
2 219037
11.0%
3 220260
11.0%
4 220181
11.0%
5 221016
11.1%
6 219702
11.0%
7 222513
11.1%
8 222790
11.1%
9 229966
11.5%
ValueCountFrequency (%)
9 229966
11.5%
8 222790
11.1%
7 222513
11.1%
6 219702
11.0%
5 221016
11.1%
4 220181
11.0%
3 220260
11.0%
2 219037
11.0%
1 224532
11.2%
Distinct173790
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size130.7 MiB
Minimum2019-08-17 15:21:39.080371
Maximum2024-08-15 15:21:39.287115
2025-01-02T16:02:09.950134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:02:10.228223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Customer Feedback
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing130100
Missing (%)6.5%
Memory size233.0 MiB
Average
629122 
Poor
625952 
Good
614826 

Length

Max length7
Median length4
Mean length5.0093406
Min length4

Characters and Unicode

Total characters9366966
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowAverage
3rd rowGood
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Average 629122
31.5%
Poor 625952
31.3%
Good 614826
30.7%
(Missing) 130100
 
6.5%

Length

2025-01-02T16:02:10.581572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:10.801708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
average 629122
33.6%
poor 625952
33.5%
good 614826
32.9%

Most occurring characters

ValueCountFrequency (%)
o 2481556
26.5%
e 1258244
13.4%
r 1255074
13.4%
A 629122
 
6.7%
v 629122
 
6.7%
a 629122
 
6.7%
g 629122
 
6.7%
P 625952
 
6.7%
G 614826
 
6.6%
d 614826
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9366966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2481556
26.5%
e 1258244
13.4%
r 1255074
13.4%
A 629122
 
6.7%
v 629122
 
6.7%
a 629122
 
6.7%
g 629122
 
6.7%
P 625952
 
6.7%
G 614826
 
6.6%
d 614826
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9366966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2481556
26.5%
e 1258244
13.4%
r 1255074
13.4%
A 629122
 
6.7%
v 629122
 
6.7%
a 629122
 
6.7%
g 629122
 
6.7%
P 625952
 
6.7%
G 614826
 
6.6%
d 614826
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9366966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2481556
26.5%
e 1258244
13.4%
r 1255074
13.4%
A 629122
 
6.7%
v 629122
 
6.7%
a 629122
 
6.7%
g 629122
 
6.7%
P 625952
 
6.7%
G 614826
 
6.6%
d 614826
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 MiB
True
1003732 
False
996268 
ValueCountFrequency (%)
True 1003732
50.2%
False 996268
49.8%
2025-01-02T16:02:11.026349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.6 MiB
Weekly
510693 
Rarely
499934 
Monthly
498230 
Daily
491143 

Length

Max length7
Median length6
Mean length6.0035435
Min length5

Characters and Unicode

Total characters12007087
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekly
2nd rowMonthly
3rd rowWeekly
4th rowDaily
5th rowWeekly

Common Values

ValueCountFrequency (%)
Weekly 510693
25.5%
Rarely 499934
25.0%
Monthly 498230
24.9%
Daily 491143
24.6%

Length

2025-01-02T16:02:11.282398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:11.539703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
weekly 510693
25.5%
rarely 499934
25.0%
monthly 498230
24.9%
daily 491143
24.6%

Most occurring characters

ValueCountFrequency (%)
l 2000000
16.7%
y 2000000
16.7%
e 1521320
12.7%
a 991077
8.3%
W 510693
 
4.3%
k 510693
 
4.3%
R 499934
 
4.2%
r 499934
 
4.2%
M 498230
 
4.1%
o 498230
 
4.1%
Other values (5) 2476976
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12007087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2000000
16.7%
y 2000000
16.7%
e 1521320
12.7%
a 991077
8.3%
W 510693
 
4.3%
k 510693
 
4.3%
R 499934
 
4.2%
r 499934
 
4.2%
M 498230
 
4.1%
o 498230
 
4.1%
Other values (5) 2476976
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12007087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2000000
16.7%
y 2000000
16.7%
e 1521320
12.7%
a 991077
8.3%
W 510693
 
4.3%
k 510693
 
4.3%
R 499934
 
4.2%
r 499934
 
4.2%
M 498230
 
4.1%
o 498230
 
4.1%
Other values (5) 2476976
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12007087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2000000
16.7%
y 2000000
16.7%
e 1521320
12.7%
a 991077
8.3%
W 510693
 
4.3%
k 510693
 
4.3%
R 499934
 
4.2%
r 499934
 
4.2%
M 498230
 
4.1%
o 498230
 
4.1%
Other values (5) 2476976
20.6%

Property Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size236.2 MiB
House
667500 
Condo
666478 
Apartment
666022 

Length

Max length9
Median length5
Mean length6.332044
Min length5

Characters and Unicode

Total characters12664088
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowApartment
5th rowHouse

Common Values

ValueCountFrequency (%)
House 667500
33.4%
Condo 666478
33.3%
Apartment 666022
33.3%

Length

2025-01-02T16:02:11.854882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T16:02:12.117708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
house 667500
33.4%
condo 666478
33.3%
apartment 666022
33.3%

Most occurring characters

ValueCountFrequency (%)
o 2000456
15.8%
e 1333522
10.5%
n 1332500
10.5%
t 1332044
10.5%
H 667500
 
5.3%
u 667500
 
5.3%
s 667500
 
5.3%
C 666478
 
5.3%
d 666478
 
5.3%
A 666022
 
5.3%
Other values (4) 2664088
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12664088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2000456
15.8%
e 1333522
10.5%
n 1332500
10.5%
t 1332044
10.5%
H 667500
 
5.3%
u 667500
 
5.3%
s 667500
 
5.3%
C 666478
 
5.3%
d 666478
 
5.3%
A 666022
 
5.3%
Other values (4) 2664088
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12664088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2000456
15.8%
e 1333522
10.5%
n 1332500
10.5%
t 1332044
10.5%
H 667500
 
5.3%
u 667500
 
5.3%
s 667500
 
5.3%
C 666478
 
5.3%
d 666478
 
5.3%
A 666022
 
5.3%
Other values (4) 2664088
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12664088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2000456
15.8%
e 1333522
10.5%
n 1332500
10.5%
t 1332044
10.5%
H 667500
 
5.3%
u 667500
 
5.3%
s 667500
 
5.3%
C 666478
 
5.3%
d 666478
 
5.3%
A 666022
 
5.3%
Other values (4) 2664088
21.0%

Premium Amount
Real number (ℝ)

Missing 

Distinct4794
Distinct (%)0.4%
Missing800000
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean1102.5448
Minimum20
Maximum4999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.7 MiB
2025-01-02T16:02:12.403322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile49
Q1514
median872
Q31509
95-th percentile2869
Maximum4999
Range4979
Interquartile range (IQR)995

Descriptive statistics

Standard deviation864.99886
Coefficient of variation (CV)0.78454757
Kurtosis1.5185856
Mean1102.5448
Median Absolute Deviation (MAD)449
Skewness1.2409155
Sum1.3230538 × 109
Variance748223.03
MonotonicityNot monotonic
2025-01-02T16:02:12.687889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 4268
 
0.2%
24 3901
 
0.2%
20 3849
 
0.2%
23 3524
 
0.2%
28 3418
 
0.2%
26 3375
 
0.2%
48 3307
 
0.2%
29 3139
 
0.2%
100 3125
 
0.2%
27 3074
 
0.2%
Other values (4784) 1165020
58.3%
(Missing) 800000
40.0%
ValueCountFrequency (%)
20 3849
0.2%
21 362
 
< 0.1%
22 1698
 
0.1%
23 3524
0.2%
24 3901
0.2%
25 4268
0.2%
26 3375
0.2%
27 3074
0.2%
28 3418
0.2%
29 3139
0.2%
ValueCountFrequency (%)
4999 1
 
< 0.1%
4997 2
 
< 0.1%
4996 1
 
< 0.1%
4994 1
 
< 0.1%
4992 1
 
< 0.1%
4991 1
 
< 0.1%
4988 18
< 0.1%
4987 5
 
< 0.1%
4986 3
 
< 0.1%
4985 2
 
< 0.1%

Interactions

2025-01-02T16:01:37.308279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:09.252116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:12.869996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:16.363901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:20.192573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:23.859553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:26.825963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:30.391536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:33.936567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:37.658720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:09.702653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:13.293812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:16.809730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:20.678293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:24.215223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:27.272678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:30.800763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:34.350768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:37.961017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:10.104274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:13.683611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:17.260952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:21.071698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:24.531015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:27.689659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:31.161867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:34.738354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:38.276565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:10.511901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:14.085726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:17.688074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:21.486621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:24.859251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:28.112412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:31.557183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:35.167735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:38.550246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:10.854242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:14.422719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:18.042989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:21.976200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:25.183871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:28.472771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:31.875859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:35.508508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:38.861370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:11.264278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:14.820047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:18.455815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:22.383962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:25.495855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:28.871647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:32.250796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:35.880061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:39.173941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:11.656508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:15.208777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:18.848134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:22.770922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:25.811355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:29.271384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:32.636333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:36.263211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:39.503918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:12.121543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:15.626614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:19.314271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:23.192913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:26.132248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:29.683226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:33.018811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:36.647195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:39.847002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:12.448464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:15.954716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:19.701976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:23.518306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:26.411488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:30.006497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:33.351360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T16:01:36.975007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-02T16:02:12.908870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AgeAnnual IncomeCredit ScoreCustomer FeedbackEducation LevelExercise FrequencyGenderHealth ScoreInsurance DurationLocationMarital StatusNumber of DependentsOccupationPolicy TypePremium AmountPrevious ClaimsProperty TypeSmoking StatusVehicle Ageid
Age1.0000.0000.0020.0000.0000.0010.000-0.000-0.0010.0000.0020.0000.0010.000-0.0020.0010.0010.000-0.002-0.000
Annual Income0.0001.000-0.1510.0020.0020.0030.0000.013-0.0010.0000.0010.0020.0010.002-0.061-0.0000.0020.000-0.0010.001
Credit Score0.002-0.1511.0000.0010.0030.0000.0020.0100.0000.0020.0030.0040.0030.001-0.0440.0380.0020.002-0.0000.001
Customer Feedback0.0000.0020.0011.0000.0010.0030.0000.0040.0010.0010.0010.0000.0020.0000.0010.0030.0020.0000.0000.000
Education Level0.0000.0020.0030.0011.0000.0010.0010.0050.0010.0020.0010.0010.0010.0010.0020.0020.0030.0000.0000.000
Exercise Frequency0.0010.0030.0000.0030.0011.0000.0010.0040.0010.0010.0010.0010.0020.0020.0010.0010.0010.0000.0020.001
Gender0.0000.0000.0020.0000.0010.0011.0000.0060.0010.0010.0020.0000.0000.0010.0020.0000.0010.0030.0010.001
Health Score-0.0000.0130.0100.0040.0050.0040.0061.0000.0020.0050.0040.0050.0050.0020.0160.0030.0000.003-0.0010.000
Insurance Duration-0.001-0.0010.0000.0010.0010.0010.0010.0021.0000.0020.0020.0020.0030.000-0.0000.0020.0020.0010.003-0.000
Location0.0000.0000.0020.0010.0020.0010.0010.0050.0021.0000.0020.0010.0010.0010.0020.0000.0000.0010.0010.002
Marital Status0.0020.0010.0030.0010.0010.0010.0020.0040.0020.0021.0000.0010.0020.0000.0000.0030.0020.0020.0010.000
Number of Dependents0.0000.0020.0040.0000.0010.0010.0000.0050.0020.0010.0011.0000.0000.0000.0040.0050.0020.0010.0020.001
Occupation0.0010.0010.0030.0020.0010.0020.0000.0050.0030.0010.0020.0001.0000.0010.0040.0010.0030.0000.0000.001
Policy Type0.0000.0020.0010.0000.0010.0020.0010.0020.0000.0010.0000.0000.0011.0000.0000.0020.0020.0010.0000.000
Premium Amount-0.002-0.061-0.0440.0010.0020.0010.0020.016-0.0000.0020.0000.0040.0040.0001.0000.0450.0020.0030.0010.001
Previous Claims0.001-0.0000.0380.0030.0020.0010.0000.0030.0020.0000.0030.0050.0010.0020.0451.0000.0020.001-0.0020.001
Property Type0.0010.0020.0020.0020.0030.0010.0010.0000.0020.0000.0020.0020.0030.0020.0020.0021.0000.0010.0030.000
Smoking Status0.0000.0000.0020.0000.0000.0000.0030.0030.0010.0010.0020.0010.0000.0010.0030.0010.0011.0000.0020.000
Vehicle Age-0.002-0.001-0.0000.0000.0000.0020.001-0.0010.0030.0010.0010.0020.0000.0000.001-0.0020.0030.0021.000-0.000
id-0.0000.0010.0010.0000.0000.0010.0010.000-0.0000.0020.0000.0010.0010.0000.0010.0010.0000.000-0.0001.000

Missing values

2025-01-02T16:01:41.117880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T16:01:45.355555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-02T16:01:58.239984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idAgeGenderAnnual IncomeMarital StatusNumber of DependentsEducation LevelOccupationHealth ScoreLocationPolicy TypePrevious ClaimsVehicle AgeCredit ScoreInsurance DurationPolicy Start DateCustomer FeedbackSmoking StatusExercise FrequencyProperty TypePremium Amount
train0019.0Female10049.0Married1.0Bachelor'sSelf-Employed22.598761UrbanPremium2.017.0372.05.02023-12-23 15:21:39.134960PoorNoWeeklyHouse2869.0
1139.0Female31678.0Divorced3.0Master'sNaN15.569731RuralComprehensive1.012.0694.02.02023-06-12 15:21:39.111551AverageYesMonthlyHouse1483.0
2223.0Male25602.0Divorced3.0High SchoolSelf-Employed47.177549SuburbanPremium1.014.0NaN3.02023-09-30 15:21:39.221386GoodYesWeeklyHouse567.0
3321.0Male141855.0Married2.0Bachelor'sNaN10.938144RuralBasic1.00.0367.01.02024-06-12 15:21:39.226954PoorYesDailyApartment765.0
4421.0Male39651.0Single1.0Bachelor'sSelf-Employed20.376094RuralPremium0.08.0598.04.02021-12-01 15:21:39.252145PoorYesWeeklyHouse2022.0
5529.0Male45963.0Married1.0Bachelor'sNaN33.053198UrbanPremium2.04.0614.05.02022-05-20 15:21:39.207847AverageNoWeeklyHouse3202.0
6641.0Male40336.0Married0.0PhDNaNNaNRuralBasic2.08.0807.06.02020-02-21 15:21:39.219432PoorNoWeeklyHouse439.0
7748.0Female127237.0Divorced2.0High SchoolEmployed5.769783SuburbanComprehensive1.011.0398.05.02022-08-08 15:21:39.181605AverageNoRarelyCondo111.0
8821.0Male1733.0Divorced3.0Bachelor'sNaN17.869551UrbanPremium1.010.0685.08.02020-12-14 15:21:39.198406AverageNoMonthlyCondo213.0
9944.0Male52447.0Married2.0Master'sEmployed20.473718UrbanComprehensive1.09.0635.03.02020-08-02 15:21:39.144722PoorNoDailyCondo64.0
idAgeGenderAnnual IncomeMarital StatusNumber of DependentsEducation LevelOccupationHealth ScoreLocationPolicy TypePrevious ClaimsVehicle AgeCredit ScoreInsurance DurationPolicy Start DateCustomer FeedbackSmoking StatusExercise FrequencyProperty TypePremium Amount
test799990199999025.0Male33991.0Divorced2.0Master'sNaN5.081818SuburbanBasic0.012.0NaN4.02023-10-03 15:21:39.102694AverageYesMonthlyCondoNaN
799991199999126.0Female90883.0Divorced2.0High SchoolNaN11.275420RuralComprehensive0.019.0494.05.02021-03-14 15:21:39.170099PoorNoMonthlyCondoNaN
799992199999233.0Female788.0Married1.0Bachelor'sNaN47.921197UrbanPremium0.018.0722.05.02024-05-28 15:21:39.123711AverageNoMonthlyHouseNaN
799993199999352.0Female25426.0Divorced4.0Bachelor'sSelf-Employed39.792397SuburbanComprehensiveNaN12.0702.01.02019-08-21 15:21:39.087123PoorYesDailyCondoNaN
799994199999423.0Female71758.0Single3.0PhDSelf-Employed22.837951SuburbanBasic2.05.0452.06.02020-06-08 15:21:39.256696GoodYesMonthlyCondoNaN
799995199999550.0Female38782.0Married1.0Bachelor'sNaN14.498639RuralPremiumNaN8.0309.02.02021-07-09 15:21:39.184157AverageYesDailyCondoNaN
7999961999996NaNFemale73462.0Single0.0Master'sNaN8.145748RuralBasic2.00.0NaN2.02023-03-28 15:21:39.250151GoodNoDailyApartmentNaN
799997199999726.0Female35178.0Single0.0Master'sEmployed6.636583UrbanComprehensiveNaN10.0NaN6.02019-09-30 15:21:39.132191PoorNoMonthlyApartmentNaN
799998199999834.0Female45661.0Single3.0Master'sNaN15.937248UrbanPremium2.017.0467.07.02022-05-09 15:21:39.253660AverageNoWeeklyCondoNaN
799999199999925.0Male24843.0Divorced3.0High SchoolNaN24.893939SuburbanComprehensiveNaN15.0NaN8.02021-05-18 15:21:39.108562GoodNoRarelyHouseNaN